DocumentCode
468982
Title
The fault diagnosis system with self-repair function for screw oil pump based on wavelet neural network
Author
Tian, Jing-wen ; Gao, Mei-juan ; Zhou, Hao ; Li, Kai
Author_Institution
Beijing Union Univ., Beijing
Volume
2
fYear
2007
fDate
2-4 Nov. 2007
Firstpage
699
Lastpage
704
Abstract
Considering the issues that the relationship between the fault of screw oil pump existent and fault information is a complicated and nonlinear system, and the wavelet neural network has the advantages of both wavelet analysis and neural network, a fault diagnosis system with self-repair function for screw oil pump based on wavelet neural network is presented in this paper. Moreover, we adopt a method of reduce the number of the wavelet basic function by analysis the sparse property of sample data, and the learning algorithm based on the gradient descent was used to train network. With the ability of strong self-learning and function approach and fast convergence rate of wavelet neural network, the diagnosis system can truly diagnose the fault of screw oil pump by learning the fault information. The real diagnosis results show that this system is feasible and effective.
Keywords
fault diagnosis; gradient methods; learning (artificial intelligence); neural nets; nonlinear systems; petroleum industry; pumps; self-adjusting systems; wavelet transforms; complicated system; fault diagnosis system; gradient descent; learning algorithm; network training; nonlinear system; screw oil pump; self-learning approach; self-repair function; wavelet basic function; wavelet neural network; Artificial neural networks; Chemical analysis; Fasteners; Fault diagnosis; Information analysis; Neural networks; Pattern analysis; Petroleum; Production; Wavelet analysis; Fault diagnosis; screw oil pump; self-repair function; wavelet neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-1065-1
Electronic_ISBN
978-1-4244-1066-8
Type
conf
DOI
10.1109/ICWAPR.2007.4420759
Filename
4420759
Link To Document